A new method for short duration transient detection in radio images: Searching for transient sources in MeerKAT data of NGC 5068
Bibliographic record
Abstract
Transient surveys are a vital tool in exploring the dynamic universe, with radio transients acting as beacons for explosive and highly energetic astrophysical phenomena. However, performing commensal transient surveys using radio imaging can require a significant amount of computing power, data storage and time. With the instrumentation available to us, and with new and exciting radio interferometers in development, it is essential that we develop efficient methods to probe the radio transient sky. In this paper, we present results from an commensal short duration transient survey, on time scales of 8 seconds, 128 seconds and 1 hour, using data from the MeerKAT radio telescope. The dataset used was obtained as part of a galaxy observing campaign, and we focus on the field of NGC 5068. We present a quick, wide field imaging strategy to enable fast imaging of large datasets, and develop methods to efficiently filter detected transient candidates. No transient candidates were identified on the time scales of 8 seconds, 128 seconds and 1 hour, leading to competitive limits on the transient surface densities of $6.7{\times}10^{-5}$ deg$^{-1}$, $1.1{\times}10^{-3}$ deg$^{-1}$, and $3.2{\times}10^{-2}$ deg$^{-1}$ at sensitivities of 56.4 mJy, 19.2 mJy, and 3.9 mJy for the respective time scales. We find one possible candidate that could be associated with a stellar flare, that was rejected due to strict image quality control. Further short time-scale radio observations of this candidate could give definite results to its origin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".